Frontier AI systems have the potential to transform biology research and drug discovery by intelligently navigating vast combinatorial search spaces (e.g., a single 100-amino-acid protein spans at least 20100 possible sequences) calling for dynamic multi-candidate exploration. Our long-term research vision is that AI agents will not only ideate and analyze, but also run lab experiments to test their hypotheses, learn from the results, and self-improve to solve complex problems — all driven by human scientific agency.
Realizing this vision requires solving two intertwined challenges: First, frontier models still do not reason reliably about 3-dimensional geometry and physical state — which poses a problem in the automation of the specialized liquid-handling robots that move reagents, track volumes, and execute molecular biology protocols in a wet lab. Second, while agentic hypothesis generation and in-silico design are advancing rapidly, needing repeated cycles of real-world feedback to validate the proposals and navigate vast search spaces, real-world wet-lab experiments have not kept pace, making experimental readouts more of a bottleneck than ever.
In this post we share three research projects highlighting our progress on bridging the gap between in-silico AI exploration and real-world wet-lab experimentation:
- Enabling AI agents to perform safe, resource efficient experimentation grounded in physical measurements by using the VERID framework.
- Demonstrating closed-loop agentic scientific discoveries on our prototypes for aptamer and de-novo protein design.
